With the prevalence of digital imaging devices (e.g., digital cameras) and the Internet, effective and efficient image retrieval techniques have become more important in commercial and academic applications. With an ever increasing number of digital images being made available, there becomes an increasing need to effectively retrieve relevant digital images desired by a user.
Issues can exist with efficiently retrieving digital images. An issue is query formulation. In other words, how can an image retrieval system translate an implicit query formed in a user's mind? For example, the user may have a particular image in mind; however, there may not be an explicit way for the user to express such a thought or query through an image retrieval system. An explicit text query may not be sufficient for a user's implicit thought or query. Another issue can be query matching. Query matching involves finding relevant images that best fit an implicit or explicit query. For example, a user may desire to retrieve similar images that are based on the implicit query.
Typical query methods based on text or content may be insufficient to support a user query based on an implicit and complex scene. For example, a user may desire to search for image(s) that include a “couple by the sea at sunset with a mountain in the background.” Current query search techniques can fall short of finding relevant images based on the user's thoughts.
Interactive query search techniques have been developed; however, such techniques may be one sided and only consider how to leverage users' efforts to catch their intentions, rather than to provide a way to allow users to express their queries by leveraging an available image database(s). Furthermore, such techniques may also use only one type of interaction. For example, certain relevance feedback approaches may only provide an interactive indication from users to tell whether retrieved image results are relevant or not.